Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations2920
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.3 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

average-wind-speed-(period) is highly overall correlated with wind-speedHigh correlation
distance-to-solar-noon is highly overall correlated with power-generatedHigh correlation
humidity is highly overall correlated with power-generatedHigh correlation
power-generated is highly overall correlated with distance-to-solar-noon and 1 other fieldsHigh correlation
wind-speed is highly overall correlated with average-wind-speed-(period)High correlation
average-wind-speed-(period) has 375 (12.8%) zeros Zeros
power-generated has 1320 (45.2%) zeros Zeros

Reproduction

Analysis started2025-01-31 03:07:26.389276
Analysis finished2025-01-31 03:07:45.004925
Duration18.62 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

distance-to-solar-noon
Real number (ℝ)

High correlation 

Distinct2660
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50329403
Minimum0.050400916
Maximum1.1413613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:45.214200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.050400916
5-th percentile0.063528533
Q10.24371398
median0.4789569
Q30.73952751
95-th percentile1.0257968
Maximum1.1413613
Range1.0909603
Interquartile range (IQR)0.49581353

Descriptive statistics

Standard deviation0.29802354
Coefficient of variation (CV)0.592146
Kurtosis-0.98730595
Mean0.50329403
Median Absolute Deviation (MAD)0.26060232
Skewness0.21145184
Sum1469.6186
Variance0.088818033
MonotonicityNot monotonic
2025-01-31T03:07:45.597887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.257046223 4
 
0.1%
0.350620068 4
 
0.1%
0.05524239 4
 
0.1%
0.258173619 4
 
0.1%
0.461104848 4
 
0.1%
0.664036077 4
 
0.1%
0.757609921 4
 
0.1%
0.554678692 4
 
0.1%
0.351747463 4
 
0.1%
0.148816234 4
 
0.1%
Other values (2650) 2880
98.6%
ValueCountFrequency (%)
0.050400916 2
0.1%
0.050516648 1
< 0.1%
0.050574713 1
< 0.1%
0.050691244 1
< 0.1%
0.050749712 1
< 0.1%
0.050808314 1
< 0.1%
0.050925926 1
< 0.1%
0.051044084 2
0.1%
0.05107832 1
< 0.1%
0.051162791 1
< 0.1%
ValueCountFrequency (%)
1.141361257 2
0.1%
1.141114983 2
0.1%
1.140869565 2
0.1%
1.140625 2
0.1%
1.140381282 1
< 0.1%
1.139616056 2
0.1%
1.138408304 1
< 0.1%
1.138169257 1
< 0.1%
1.137870855 2
0.1%
1.136206897 2
0.1%

temperature
Real number (ℝ)

Distinct37
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.468493
Minimum42
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:45.961715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile48
Q153
median59
Q363
95-th percentile70
Maximum78
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8412003
Coefficient of variation (CV)0.11700661
Kurtosis-0.28740218
Mean58.468493
Median Absolute Deviation (MAD)5
Skewness0.12591613
Sum170728
Variance46.802022
MonotonicityNot monotonic
2025-01-31T03:07:46.276676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
62 240
 
8.2%
55 184
 
6.3%
61 168
 
5.8%
59 160
 
5.5%
64 152
 
5.2%
63 152
 
5.2%
56 136
 
4.7%
54 136
 
4.7%
57 128
 
4.4%
60 128
 
4.4%
Other values (27) 1336
45.8%
ValueCountFrequency (%)
42 8
 
0.3%
43 24
 
0.8%
44 8
 
0.3%
45 16
 
0.5%
46 24
 
0.8%
47 56
1.9%
48 56
1.9%
49 128
4.4%
50 80
2.7%
51 96
3.3%
ValueCountFrequency (%)
78 8
 
0.3%
77 8
 
0.3%
76 8
 
0.3%
75 16
 
0.5%
74 24
0.8%
73 24
0.8%
72 8
 
0.3%
71 32
1.1%
70 32
1.1%
69 48
1.6%

wind-direction
Real number (ℝ)

Distinct35
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.953425
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:46.576673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q125
median27
Q329
95-th percentile31
Maximum36
Range35
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.915178
Coefficient of variation (CV)0.2771234
Kurtosis1.8566217
Mean24.953425
Median Absolute Deviation (MAD)2
Skewness-1.6295192
Sum72864
Variance47.819687
MonotonicityNot monotonic
2025-01-31T03:07:46.974153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
27 528
18.1%
29 496
17.0%
28 464
15.9%
30 288
9.9%
26 216
 
7.4%
32 88
 
3.0%
14 64
 
2.2%
31 56
 
1.9%
20 56
 
1.9%
13 48
 
1.6%
Other values (25) 616
21.1%
ValueCountFrequency (%)
1 8
 
0.3%
2 8
 
0.3%
3 24
0.8%
4 24
0.8%
5 16
0.5%
6 24
0.8%
7 24
0.8%
8 8
 
0.3%
9 24
0.8%
10 32
1.1%
ValueCountFrequency (%)
36 16
 
0.5%
34 16
 
0.5%
33 16
 
0.5%
32 88
 
3.0%
31 56
 
1.9%
30 288
9.9%
29 496
17.0%
28 464
15.9%
27 528
18.1%
26 216
7.4%

wind-speed
Real number (ℝ)

High correlation 

Distinct159
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.096986
Minimum1.1
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:47.363810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.6
Q16.6
median10
Q313.1
95-th percentile18.7
Maximum26.6
Range25.5
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.8381851
Coefficient of variation (CV)0.4791712
Kurtosis0.078605706
Mean10.096986
Median Absolute Deviation (MAD)3.3
Skewness0.41685133
Sum29483.2
Variance23.408035
MonotonicityNot monotonic
2025-01-31T03:07:47.577670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 56
 
1.9%
6.9 48
 
1.6%
10.4 48
 
1.6%
13 48
 
1.6%
6.6 48
 
1.6%
12.9 40
 
1.4%
8.1 40
 
1.4%
3.8 40
 
1.4%
10.3 40
 
1.4%
14.6 40
 
1.4%
Other values (149) 2472
84.7%
ValueCountFrequency (%)
1.1 8
 
0.3%
1.2 8
 
0.3%
1.3 16
0.5%
1.5 8
 
0.3%
1.8 8
 
0.3%
1.9 16
0.5%
2 16
0.5%
2.1 8
 
0.3%
2.3 24
0.8%
2.4 24
0.8%
ValueCountFrequency (%)
26.6 8
0.3%
25.1 8
0.3%
24.4 8
0.3%
23.5 8
0.3%
23.3 8
0.3%
22.1 8
0.3%
21.5 8
0.3%
21.2 8
0.3%
21.1 8
0.3%
20.8 8
0.3%

sky-cover
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
1
776 
4
598 
3
580 
0
518 
2
448 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

Length

2025-01-31T03:07:47.757994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T03:07:47.886814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

Most occurring characters

ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 776
26.6%
4 598
20.5%
3 580
19.9%
0 518
17.7%
2 448
15.3%

visibility
Real number (ℝ)

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5577055
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:48.034421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3838837
Coefficient of variation (CV)0.14479246
Kurtosis15.718494
Mean9.5577055
Median Absolute Deviation (MAD)0
Skewness-3.8624462
Sum27908.5
Variance1.9151342
MonotonicityNot monotonic
2025-01-31T03:07:48.174752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 2491
85.3%
9 169
 
5.8%
8 73
 
2.5%
4 43
 
1.5%
6 42
 
1.4%
7 36
 
1.2%
5 29
 
1.0%
3 14
 
0.5%
1.5 6
 
0.2%
0.25 4
 
0.1%
Other values (7) 13
 
0.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.25 4
 
0.1%
0.5 2
 
0.1%
0.75 1
 
< 0.1%
1.25 1
 
< 0.1%
1.5 6
0.2%
1.75 2
 
0.1%
2 2
 
0.1%
2.5 4
 
0.1%
3 14
0.5%
ValueCountFrequency (%)
10 2491
85.3%
9 169
 
5.8%
8 73
 
2.5%
7 36
 
1.2%
6 42
 
1.4%
5 29
 
1.0%
4 43
 
1.5%
3 14
 
0.5%
2.5 4
 
0.1%
2 2
 
0.1%

humidity
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.513699
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:48.360787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile44.95
Q165
median77
Q384
95-th percentile93
Maximum100
Range86
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.077139
Coefficient of variation (CV)0.20509292
Kurtosis0.87221899
Mean73.513699
Median Absolute Deviation (MAD)9
Skewness-0.95560721
Sum214660
Variance227.32013
MonotonicityNot monotonic
2025-01-31T03:07:48.585089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 218
 
7.5%
90 173
 
5.9%
80 161
 
5.5%
86 156
 
5.3%
93 141
 
4.8%
77 127
 
4.3%
75 107
 
3.7%
78 106
 
3.6%
70 106
 
3.6%
87 106
 
3.6%
Other values (65) 1519
52.0%
ValueCountFrequency (%)
14 1
 
< 0.1%
17 3
0.1%
18 2
 
0.1%
19 2
 
0.1%
20 1
 
< 0.1%
21 5
0.2%
23 1
 
< 0.1%
24 3
0.1%
25 3
0.1%
26 3
0.1%
ValueCountFrequency (%)
100 11
 
0.4%
96 71
 
2.4%
93 141
4.8%
90 173
5.9%
89 53
 
1.8%
87 106
3.6%
86 156
5.3%
84 71
 
2.4%
83 218
7.5%
82 2
 
0.1%

average-wind-speed-(period)
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.129154
Minimum0
Maximum40
Zeros375
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:48.758822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q315
95-th percentile23
Maximum40
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2615465
Coefficient of variation (CV)0.71689567
Kurtosis0.015341512
Mean10.129154
Median Absolute Deviation (MAD)5
Skewness0.62291034
Sum29567
Variance52.730058
MonotonicityNot monotonic
2025-01-31T03:07:48.928819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 375
12.8%
3 248
 
8.5%
5 217
 
7.4%
6 207
 
7.1%
8 179
 
6.1%
7 165
 
5.7%
10 155
 
5.3%
14 153
 
5.2%
9 153
 
5.2%
11 152
 
5.2%
Other values (22) 915
31.3%
ValueCountFrequency (%)
0 375
12.8%
3 248
8.5%
5 217
7.4%
6 207
7.1%
7 165
5.7%
8 179
6.1%
9 153
5.2%
10 155
5.3%
11 152
5.2%
13 131
 
4.5%
ValueCountFrequency (%)
40 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 2
 
0.1%
33 3
 
0.1%
32 6
 
0.2%
31 11
0.4%
30 6
 
0.2%
29 10
0.3%
28 18
0.6%

average-pressure-(period)
Real number (ℝ)

Distinct90
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.01776
Minimum29.48
Maximum30.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:49.114403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29.48
5-th percentile29.81
Q129.92
median30
Q330.11
95-th percentile30.27
Maximum30.53
Range1.05
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14200583
Coefficient of variation (CV)0.0047307272
Kurtosis0.35230933
Mean30.01776
Median Absolute Deviation (MAD)0.09
Skewness0.44138375
Sum87651.86
Variance0.020165657
MonotonicityNot monotonic
2025-01-31T03:07:49.309124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.97 114
 
3.9%
29.96 112
 
3.8%
29.94 108
 
3.7%
29.98 98
 
3.4%
29.95 94
 
3.2%
29.91 91
 
3.1%
30 86
 
2.9%
29.93 86
 
2.9%
29.92 84
 
2.9%
30.02 83
 
2.8%
Other values (80) 1964
67.3%
ValueCountFrequency (%)
29.48 1
 
< 0.1%
29.52 1
 
< 0.1%
29.56 1
 
< 0.1%
29.59 1
 
< 0.1%
29.61 1
 
< 0.1%
29.62 1
 
< 0.1%
29.64 3
0.1%
29.65 1
 
< 0.1%
29.67 1
 
< 0.1%
29.68 1
 
< 0.1%
ValueCountFrequency (%)
30.53 1
 
< 0.1%
30.52 1
 
< 0.1%
30.51 2
 
0.1%
30.5 6
0.2%
30.49 5
0.2%
30.48 1
 
< 0.1%
30.47 3
0.1%
30.43 2
 
0.1%
30.42 2
 
0.1%
30.4 2
 
0.1%

power-generated
Real number (ℝ)

High correlation  Zeros 

Distinct1529
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6979.8462
Minimum0
Maximum36580
Zeros1320
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2025-01-31T03:07:49.834643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median404
Q312723.5
95-th percentile29583.25
Maximum36580
Range36580
Interquartile range (IQR)12723.5

Descriptive statistics

Standard deviation10312.336
Coefficient of variation (CV)1.4774446
Kurtosis0.34220612
Mean6979.8462
Median Absolute Deviation (MAD)404
Skewness1.3070351
Sum20381151
Variance1.0634428 × 108
MonotonicityNot monotonic
2025-01-31T03:07:50.046373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1320
45.2%
3 3
 
0.1%
229 3
 
0.1%
738 3
 
0.1%
114 2
 
0.1%
29303 2
 
0.1%
21473 2
 
0.1%
915 2
 
0.1%
2232 2
 
0.1%
467 2
 
0.1%
Other values (1519) 1579
54.1%
ValueCountFrequency (%)
0 1320
45.2%
1 2
 
0.1%
2 1
 
< 0.1%
3 3
 
0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 2
 
0.1%
ValueCountFrequency (%)
36580 1
< 0.1%
36400 1
< 0.1%
36368 1
< 0.1%
35778 1
< 0.1%
35743 1
< 0.1%
35635 1
< 0.1%
35553 1
< 0.1%
35486 1
< 0.1%
35474 1
< 0.1%
35405 1
< 0.1%

Interactions

2025-01-31T03:07:42.080446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:26.932887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:28.756140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:31.460602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:33.623589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:35.158392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:36.960664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:38.457783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:40.191538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:42.293578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:27.116151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:29.003426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:31.773360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:33.780094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:35.555943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:37.106592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:38.667370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:40.349879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:42.479234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:27.282918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:29.292550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:32.042956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:33.963452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:35.724632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:37.264922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:38.844878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:40.586243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:42.693974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:27.500513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:29.780321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:32.356689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:34.130783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:35.900786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:37.462011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:39.052310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:40.762601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:42.886206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:27.676566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:30.052615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:32.679876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:34.282681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:36.068181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:37.622913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:39.227255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:40.931506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:43.093620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:27.866004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:30.340424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:32.920703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:34.446613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-31T03:07:39.396875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-31T03:07:43.329636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-31T03:07:30.647741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-31T03:07:34.632589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:36.399060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:37.913166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:39.619829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:41.557522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:43.674976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:28.307094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:30.956105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:33.261880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:34.803754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:36.624389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:38.105127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:39.808959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:41.733520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:43.961942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:28.537434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:31.196256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:33.436038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:34.988548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:36.775116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:38.275599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:39.979129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T03:07:41.902023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-31T03:07:50.206110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
average-pressure-(period)average-wind-speed-(period)distance-to-solar-noonhumiditypower-generatedsky-covertemperaturevisibilitywind-directionwind-speed
average-pressure-(period)1.000-0.3020.0760.044-0.0410.104-0.441-0.118-0.220-0.385
average-wind-speed-(period)-0.3021.000-0.196-0.2220.2420.1570.0810.1630.0590.666
distance-to-solar-noon0.076-0.1961.0000.407-0.8870.106-0.1360.064-0.064-0.143
humidity0.044-0.2220.4071.000-0.5290.226-0.163-0.343-0.115-0.022
power-generated-0.0410.242-0.887-0.5291.0000.1630.118-0.0060.0870.121
sky-cover0.1040.1570.1060.2260.1631.0000.1970.1570.2150.153
temperature-0.4410.081-0.136-0.1630.1180.1971.0000.1800.3480.146
visibility-0.1180.1630.064-0.343-0.0060.1570.1801.0000.1030.194
wind-direction-0.2200.059-0.064-0.1150.0870.2150.3480.1031.0000.092
wind-speed-0.3850.666-0.143-0.0220.1210.1530.1460.1940.0921.000

Missing values

2025-01-31T03:07:44.422822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-31T03:07:44.746065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

distance-to-solar-noontemperaturewind-directionwind-speedsky-covervisibilityhumidityaverage-wind-speed-(period)average-pressure-(period)power-generated
00.85989769287.5010.0758.029.820
10.62853569287.5010.0775.029.850
20.39717269287.5010.0700.029.895418
30.16581069287.5010.0330.029.9125477
40.06555369287.5010.0213.029.8930069
50.29691569287.5010.02023.029.8516280
60.52827869287.5010.03615.029.83515
70.75964069287.5010.0496.029.860
80.86211372296.8010.0676.029.860
90.63015572296.8010.0490.029.870
distance-to-solar-noontemperaturewind-directionwind-speedsky-covervisibilityhumidityaverage-wind-speed-(period)average-pressure-(period)power-generated
29100.523627612715.8310.08417.029.85895
29110.753512612715.8410.09013.029.870
29120.857875632713.9410.09311.029.860
29130.627401632713.9410.09011.029.860
29140.396927632713.9410.0879.029.90464
29150.166453632713.9410.07510.029.936995
29160.064020632713.9110.06615.029.9129490
29170.294494632713.9210.06821.029.8817257
29180.524968632713.9210.08117.029.87677
29190.755442632713.9110.08111.029.900